Gmnc KO quality control

Load libraries

library(Seurat)
library(Matrix)
library(dplyr)
library(RColorBrewer)
library(ggplot2)
library(ggExtra)
library(cowplot)
library(reticulate)
library(wesanderson)
library(princurve)
use_python("/usr/bin/python3")

#Set ggplot theme as classic
theme_set(theme_classic())

Load the raw counts matrix

Countdata <- Read10X("../../RawData/Gmnc_KO/outs/filtered_feature_bc_matrix/")

Raw.data <- CreateSeuratObject(counts = Countdata,
                              project = "Gmnc_KO",
                              min.cells = 3,
                              min.features = 800)

Raw.data$Barcodes <- rownames(Raw.data@meta.data)

rm(Countdata)

dim(Raw.data)

## [1] 21077 16272

Raw.data$percent.mito <- PercentageFeatureSet(Raw.data, pattern = "^mt-")
Raw.data$percent.ribo <- PercentageFeatureSet(Raw.data, pattern = "(^Rpl|^Rps|^Mrp)")

VlnPlot(object = Raw.data, features = c("nFeature_RNA","nCount_RNA", "percent.mito", "percent.ribo"), ncol= 2) & NoAxes()

# Inspect cell based on relation between nUMI and nGene detected

# Relation between nUMI and nGene detected
Cell.QC.Stat <- Raw.data@meta.data

p1 <- ggplot(Cell.QC.Stat, aes(x=nCount_RNA, y=nFeature_RNA)) + geom_point() + geom_smooth(method="lm")
p1 <- ggMarginal(p1, type = "histogram", fill="lightgrey")

p2 <- ggplot(Cell.QC.Stat, aes(x=log10(nCount_RNA), y=log10(nFeature_RNA))) + geom_point() + geom_smooth(method="lm")
p2 <- ggMarginal(p2, type = "histogram", fill="lightgrey")

plot_grid(plotlist = list(p1,p2), ncol=2, align='h', rel_widths = c(1, 1)) ; rm(p1,p2)

Cells with deviating nGene/nUMI ratio display an Erythrocyte signature

Raw.data <- AddModuleScore(Raw.data,
                           features = list(c("Hbb-bt", "Hbq1a", "Isg20", "Fech", "Snca", "Rec114")),
                           ctrl = 10,
                           name = "Erythrocyte.signature")

Cell.QC.Stat$Erythrocyte.signature <- Raw.data$Erythrocyte.signature1

gradient <- colorRampPalette(brewer.pal(n =11, name = "Spectral"))(100)

p1 <- ggplot(Cell.QC.Stat, aes(log10(nCount_RNA), y=log10(nFeature_RNA))) +
      geom_point(aes(color= Erythrocyte.signature))  + 
      scale_color_gradientn(colours=rev(gradient), name='Erythrocyte score') + theme(legend.position="none")

p2 <- ggplot(Cell.QC.Stat, aes(log10(nCount_RNA), y=log10(nFeature_RNA))) +
      geom_point(aes(color= percent.mito))  + 
      scale_color_gradientn(colours=rev(gradient), name='Percent mito') + theme(legend.position="none")

p3 <- ggplot(Cell.QC.Stat, aes(log10(nCount_RNA), y=log10(nFeature_RNA))) +
      geom_point(aes(color= percent.ribo))  + 
      scale_color_gradientn(colours=rev(gradient), name='Percent ribo') + theme(legend.position="none")

p1 + p2 + p3

## Exclude Erythrocytes

Cell.QC.Stat$Erythrocyte <- ifelse(Cell.QC.Stat$Erythrocyte.signature > 0.1, "Erythrocyte", "Not_Erythrocyte")

p2 <- ggplot(Cell.QC.Stat, aes(x=log10(nCount_RNA), y=log10(nFeature_RNA))) +
  geom_point(aes(colour = Erythrocyte)) +
  theme(legend.position="none")

ggMarginal(p2, type = "histogram", fill="lightgrey")

# Filter cells based on these thresholds
Cell.QC.Stat <- Cell.QC.Stat %>% filter(Cell.QC.Stat$Erythrocyte.signature < 0.1)

Low quality cell filtering

Filtering cells based on percentage of mitochondrial transcripts

We applied a high and low median absolute deviation (mad) thresholds to exclude outlier cells

max.mito.thr <- median(Cell.QC.Stat$percent.mito) + 3*mad(Cell.QC.Stat$percent.mito)
min.mito.thr <- median(Cell.QC.Stat$percent.mito) - 3*mad(Cell.QC.Stat$percent.mito)

p1 <- ggplot(Cell.QC.Stat, aes(x=nFeature_RNA, y=percent.mito)) +
  geom_point() +
  geom_hline(aes(yintercept = max.mito.thr), colour = "red", linetype = 2) +
  geom_hline(aes(yintercept = min.mito.thr), colour = "red", linetype = 2) +
  annotate(geom = "text", label = paste0(as.numeric(table(Cell.QC.Stat$percent.mito > max.mito.thr | Cell.QC.Stat$percent.mito < min.mito.thr)[2])," cells removed\n",
                                         as.numeric(table(Cell.QC.Stat$percent.mito > max.mito.thr | Cell.QC.Stat$percent.mito < min.mito.thr)[1])," cells remain"),
           x = 6000, y = 20)

ggMarginal(p1, type = "histogram", fill="lightgrey", bins=100) 

# Filter cells based on these thresholds
Cell.QC.Stat <- Cell.QC.Stat %>% filter(percent.mito < max.mito.thr) %>% filter(percent.mito > min.mito.thr)

Filtering cells based on number of genes and transcripts detected

Remove cells with to few gene detected or with to many UMI counts

We filter cells which are likely to be doublet based on their higher content of transcript detected as well as cell with to few genes/UMI sequenced

# Set low and hight thresholds on the number of detected genes based on the one obtain with the WT dataset
min.Genes.thr <- log10(1635)
max.Genes.thr <- log10(8069)

# Set hight threshold on the number of transcripts
max.nUMI.thr <- log10(58958)

# Gene/UMI scatter plot before filtering
p1 <- ggplot(Cell.QC.Stat, aes(x=log10(nCount_RNA), y=log10(nFeature_RNA))) +
  geom_point() +
  geom_smooth(method="lm") +
  geom_hline(aes(yintercept = min.Genes.thr), colour = "green", linetype = 2) +
  geom_hline(aes(yintercept = max.Genes.thr), colour = "green", linetype = 2) +
  geom_vline(aes(xintercept = max.nUMI.thr), colour = "red", linetype = 2)

ggMarginal(p1, type = "histogram", fill="lightgrey")

# Filter cells base on both metrics
Cell.QC.Stat <- Cell.QC.Stat %>% filter(log10(nFeature_RNA) > min.Genes.thr) %>% filter(log10(nCount_RNA) < max.nUMI.thr)

Filter cells below the main population nUMI/nGene relationship

lm.model <- lm(data = Cell.QC.Stat, formula = log10(nFeature_RNA) ~ log10(nCount_RNA))

p2 <- ggplot(Cell.QC.Stat, aes(x=log10(nCount_RNA), y=log10(nFeature_RNA))) +
  geom_point() +
  geom_smooth(method="lm") +
  geom_hline(aes(yintercept = min.Genes.thr), colour = "green", linetype = 2) +
  geom_hline(aes(yintercept = max.Genes.thr), colour = "green", linetype = 2) +
  geom_vline(aes(xintercept = max.nUMI.thr), colour = "red", linetype = 2) +
  annotate(geom = "text", label = paste0(dim(Cell.QC.Stat)[1], " QC passed cells"), x = 4, y = 3.8)

ggMarginal(p2, type = "histogram", fill="lightgrey")

## Filter the Seurat object

Raw.data <- subset(x = Raw.data, subset = Barcodes %in%  Cell.QC.Stat$Barcodes)

# Plot final QC metrics
VlnPlot(object = Raw.data, features = c("nFeature_RNA","nCount_RNA", "percent.mito", "percent.ribo"), ncol= 2) & NoAxes()

p1 <- ggplot(Raw.data@meta.data, aes(x=log10(nCount_RNA), y=log10(nFeature_RNA))) + geom_point() + geom_smooth(method="lm")
ggMarginal(p1, type = "histogram", fill="lightgrey")

rm(list = ls()[!ls() %in% "Raw.data"])

Use Scrublet to detect obvious doublets

Run Scrublet with default parameter

Export raw count matrix as input to Scrublet

#Export filtered matrix
dir.create("../../RawData/Gmnc_KO/Scrublet_inputs")

exprData <- Matrix(as.matrix(Raw.data@assays[["RNA"]]@counts), sparse = TRUE)
writeMM(exprData, "../../RawData/Gmnc_KO/Scrublet_inputs/matrix1.mtx")

## NULL

import scrublet as scr
import scipy.io
import numpy as np
import os

#Load raw counts matrix and gene list
input_dir = '../../RawData/Gmnc_KO/Scrublet_inputs'
counts_matrix = scipy.io.mmread(input_dir + '/matrix1.mtx').T.tocsc()

#Initialize Scrublet
scrub = scr.Scrublet(counts_matrix,
                     expected_doublet_rate=0.1,
                     sim_doublet_ratio=2,
                     n_neighbors = 8)

#Run the default pipeline
doublet_scores, predicted_doublets = scrub.scrub_doublets(min_counts=1, 
                                                          min_cells=3, 
                                                          min_gene_variability_pctl=85, 
                                                          n_prin_comps=25)

## Preprocessing...
## Simulating doublets...
## Embedding transcriptomes using PCA...
## Calculating doublet scores...
## Automatically set threshold at doublet score = 0.35
## Detected doublet rate = 3.7%
## Estimated detectable doublet fraction = 26.0%
## Overall doublet rate:
##  Expected   = 10.0%
##  Estimated  = 14.2%
## Elapsed time: 21.3 seconds

# Import scrublet's doublet score
Raw.data$Doubletscore <- py$doublet_scores

# Plot doublet score
ggplot(Raw.data@meta.data, aes(x = Doubletscore, stat(ndensity))) +
  geom_histogram(bins = 200, colour ="lightgrey")+
  geom_vline(xintercept = 0.15, colour = "red", linetype = 2)

# Manually set threshold at doublet score to 0.2
Raw.data$Predicted_doublets <- ifelse(py$doublet_scores > 0.15, "Doublet","Singlet")
table(Raw.data$Predicted_doublets)

## 
## Doublet Singlet 
##    2273   10215

Raw.data <- subset(x = Raw.data, subset = Predicted_doublets == "Singlet")

Generate SRING dimentionality reduction

Export counts matrix

dir.create("./SpringCoordinates")

# Export raw expression matrix and gene list to regenerate a spring plot
exprData <- Matrix(as.matrix(Raw.data@assays[["RNA"]]@counts), sparse = TRUE)
writeMM(exprData, "./SpringCoordinates/ExprData.mtx")

## NULL

Genelist <- row.names(Raw.data@assays[["RNA"]]@counts)
write.table(Genelist, "./SpringCoordinates/Genelist.csv", sep="\t", col.names = F, row.names = F, quote = F)

#Export metadata
Scrublet <- c("Scrublet", Raw.data$Predicted_doublets)
Scrublet <- paste(Scrublet, sep=",", collapse=",")

Cellgrouping <- Scrublet
write.table(Cellgrouping, "./SpringCoordinates/Cellgrouping.csv", quote =F, row.names = F, col.names = F)

Import coordinates

spring.coor <- read.table("SpringCoordinates/coordinates.txt", sep = ",", header = F, row.names = 1)
colnames(spring.coor) <- c("Spring_1", "Spring_2")

Spring.Sym <- function(x){
  x = abs(max(spring.coor$Spring_2)-x)
 }

spring.coor$Spring_2 <- sapply(spring.coor$Spring_2, function(x) Spring.Sym(x))

Raw.data$Spring_1 <- spring.coor$Spring_1
Raw.data$Spring_2 <- spring.coor$Spring_2

spring <- as.matrix(Raw.data@meta.data %>% select("Spring_1", "Spring_2"))
  
Raw.data[["spring"]] <- CreateDimReducObject(embeddings = spring, key = "Spring_", assay = DefaultAssay(Raw.data))

DimPlot(Raw.data, 
        reduction = "spring",
        pt.size = 0.5) & NoAxes()

# Sctransform normalization

Raw.data <- SCTransform(Raw.data,
                        method = "glmGamPoi",
                        vars.to.regress = c("percent.mito"),
                        verbose = T)

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Run PCA and broad clustering

Raw.data <- RunPCA(Raw.data, verbose = FALSE)

Raw.data <- FindNeighbors(Raw.data,
                          dims = 1:20,
                          k.param = 8)

Raw.data <- FindClusters(Raw.data, resolution = 0.2)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 10215
## Number of edges: 218490
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9335
## Number of communities: 9
## Elapsed time: 1 seconds

DimPlot(Raw.data,
        reduction = "spring",
        cols = c("#ebcb2e", "#9ec22f", "#a9961b", "#cc3a1b", "#d14c8d", "#4cabdc", "#5ab793", "#e7823a", "#046c9a", "#4990c9"),
        pt.size = 0.5) & NoAxes()

Raw.data$Broadclust.ident <- Raw.data$seurat_clusters

Differentiating neurons sub-clustering

Extract differentiating neurons

Neurons.data <-  subset(Raw.data, idents = 3)

DimPlot(Neurons.data,
        reduction = "spring",
        pt.size = 1,
        cols =  c("#cc3a1b")) + NoAxes()

## Fit pseudotime

fit <- principal_curve(as.matrix(Neurons.data@meta.data[,c("Spring_1", "Spring_2")]),
                       smoother='lowess',
                       trace=TRUE,
                       f = 1,
                       stretch=0)

## Starting curve---distance^2: 84204082853
## Iteration 1---distance^2: 46039577
## Iteration 2---distance^2: 42955956
## Iteration 3---distance^2: 41286356
## Iteration 4---distance^2: 40749859
## Iteration 5---distance^2: 40679459
## Iteration 6---distance^2: 40714117

#Pseudotime score
PseudotimeScore <- fit$lambda/max(fit$lambda)

if (cor(PseudotimeScore, Neurons.data@assays$SCT@data['Hmga2', ]) > 0) {
  Neurons.data$PseudotimeScore <- -(PseudotimeScore - max(PseudotimeScore))
}

cols <- brewer.pal(n =11, name = "Spectral")

ggplot(Neurons.data@meta.data, aes(Spring_1, Spring_2)) +
  geom_point(aes(color=PseudotimeScore), size=2, shape=16) + 
  scale_color_gradientn(colours=rev(cols), name='Pseudotime score')

## Regress pseudotime and cluster cells into lineages

Neurons.data <- SCTransform(Neurons.data,
                            method = "glmGamPoi",
                            vars.to.regress = c("PseudotimeScore", "nFeature_RNA"),
                            verbose = T)

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We used on transcription factors expression profile to perform lineage clustering

TFs <- read.table("TF.csv", sep = ";")[,1]

Neurons.data <- RunPCA(Neurons.data,
                       features = TFs,
                       verbose = FALSE)

Neurons.data <- FindNeighbors(Neurons.data,
                          dims = c(1:10),
                          k.param = 6)

Neurons.data <- FindClusters(Neurons.data, resolution = 0.2)

## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
## 
## Number of nodes: 1659
## Number of edges: 22850
## 
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8550
## Number of communities: 3
## Elapsed time: 0 seconds

DimPlot(Neurons.data,
        reduction = "spring",
        cols = c("#ebcb2e", "#9ec22f", "#a9961b", "#cc3a1b", "#d14c8d", "#4cabdc", "#5ab793", "#e7823a", "#046c9a", "#4990c9"),
        pt.size = 0.5) & NoAxes()

FeaturePlot(object = Neurons.data,
            features = c("Foxg1", "Zfpm2",
                         "Lhx1", "Zic5", "Zfp503"),
            pt.size = 0.2,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes() & NoLegend()

Raw.data$Broadclust.ident <- sapply(Raw.data$Barcodes,
                              FUN = function(x) {
                                if (x %in% Neurons.data$Barcodes) {
                                  x = paste0("Neuron_",Neurons.data@meta.data[x, "seurat_clusters"])
                                } else {
                                  x = Raw.data@meta.data[x, "Broadclust.ident"]
                                  }
                              })

DimPlot(Raw.data,
        reduction = "spring",
        group.by = "Broadclust.ident",
        cols = c("#ebcb2e", "#9ec22f", "#a9961b", "#cc3a1b", "#d14c8d", "#4cabdc", "#5ab793", "grey90", "#e7823a", "#046c9a", "#4990c9"),
        pt.size = 0.5) & NoAxes()

Plot marker genes found in the WT dataset

FeaturePlot(object = Raw.data,
            features = c("Tbr1","Foxg1", "Trp73", "Htr2c",
                         "Shisa2", "Wif1", "Rassf4","Dkk3",
                         "Dlk1", "Sulf1", "Ttr", "Fgf8", "Fgf17",
                         "Foxc1", "C1qb"),
            pt.size = 0.2,
            cols = c("grey90", brewer.pal(9,"YlGnBu")),
            reduction = "spring",
            order = T) & NoAxes() & NoLegend()

Save the object

saveRDS(Raw.data, "./GmncKO.cells.RDS")

Session Info

#date
format(Sys.time(), "%d %B, %Y, %H,%M")

## [1] "22 avril, 2022, 15,15"

#Packages used
sessionInfo()

## R version 4.1.3 (2022-03-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /home/matthieu/anaconda3/lib/libmkl_rt.so.1
## 
## locale:
##  [1] LC_CTYPE=fr_FR.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=fr_FR.UTF-8        LC_COLLATE=fr_FR.UTF-8    
##  [5] LC_MONETARY=fr_FR.UTF-8    LC_MESSAGES=fr_FR.UTF-8   
##  [7] LC_PAPER=fr_FR.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=fr_FR.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] princurve_2.1.6    wesanderson_0.3.6  reticulate_1.22    cowplot_1.1.1     
##  [5] ggExtra_0.9        ggplot2_3.3.5      RColorBrewer_1.1-2 dplyr_1.0.7       
##  [9] Matrix_1.4-1       SeuratObject_4.0.4 Seurat_4.0.5      
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15                  colorspace_2.0-2           
##   [3] deldir_1.0-6                ellipsis_0.3.2             
##   [5] ggridges_0.5.3              XVector_0.34.0             
##   [7] GenomicRanges_1.46.1        spatstat.data_2.1-0        
##   [9] farver_2.1.0                leiden_0.3.9               
##  [11] listenv_0.8.0               ggrepel_0.9.1              
##  [13] fansi_0.5.0                 sparseMatrixStats_1.6.0    
##  [15] codetools_0.2-18            splines_4.1.3              
##  [17] knitr_1.36                  polyclip_1.10-0            
##  [19] jsonlite_1.7.2              ica_1.0-2                  
##  [21] cluster_2.1.3               png_0.1-7                  
##  [23] uwot_0.1.10                 shiny_1.7.1                
##  [25] sctransform_0.3.2           spatstat.sparse_2.0-0      
##  [27] compiler_4.1.3              httr_1.4.2                 
##  [29] assertthat_0.2.1            fastmap_1.1.0              
##  [31] lazyeval_0.2.2              later_1.3.0                
##  [33] htmltools_0.5.2             tools_4.1.3                
##  [35] igraph_1.2.11               GenomeInfoDbData_1.2.7     
##  [37] gtable_0.3.0                glue_1.5.1                 
##  [39] RANN_2.6.1                  reshape2_1.4.4             
##  [41] Rcpp_1.0.8                  Biobase_2.54.0             
##  [43] scattermore_0.7             jquerylib_0.1.4            
##  [45] vctrs_0.3.8                 nlme_3.1-153               
##  [47] DelayedMatrixStats_1.16.0   lmtest_0.9-39              
##  [49] xfun_0.28                   stringr_1.4.0              
##  [51] globals_0.14.0              mime_0.12                  
##  [53] miniUI_0.1.1.1              lifecycle_1.0.1            
##  [55] irlba_2.3.3                 goftest_1.2-3              
##  [57] future_1.23.0               zlibbioc_1.40.0            
##  [59] MASS_7.3-56                 zoo_1.8-9                  
##  [61] scales_1.1.1                spatstat.core_2.3-1        
##  [63] MatrixGenerics_1.6.0        promises_1.2.0.1           
##  [65] spatstat.utils_2.2-0        SummarizedExperiment_1.24.0
##  [67] parallel_4.1.3              yaml_2.2.1                 
##  [69] pbapply_1.5-0               gridExtra_2.3              
##  [71] sass_0.4.0                  rpart_4.1.16               
##  [73] stringi_1.7.6               highr_0.9                  
##  [75] S4Vectors_0.32.3            BiocGenerics_0.40.0        
##  [77] GenomeInfoDb_1.30.0         bitops_1.0-7               
##  [79] rlang_0.4.12                pkgconfig_2.0.3            
##  [81] matrixStats_0.61.0          glmGamPoi_1.6.0            
##  [83] evaluate_0.14               lattice_0.20-45            
##  [85] ROCR_1.0-11                 purrr_0.3.4                
##  [87] tensor_1.5                  labeling_0.4.2             
##  [89] patchwork_1.1.1             htmlwidgets_1.5.4          
##  [91] tidyselect_1.1.1            parallelly_1.29.0          
##  [93] RcppAnnoy_0.0.19            plyr_1.8.6                 
##  [95] magrittr_2.0.2              R6_2.5.1                   
##  [97] IRanges_2.28.0              generics_0.1.1             
##  [99] DelayedArray_0.20.0         DBI_1.1.1                  
## [101] pillar_1.6.4                withr_2.4.3                
## [103] mgcv_1.8-40                 fitdistrplus_1.1-6         
## [105] RCurl_1.98-1.5              survival_3.2-13            
## [107] abind_1.4-5                 tibble_3.1.6               
## [109] future.apply_1.8.1          crayon_1.4.2               
## [111] KernSmooth_2.23-20          utf8_1.2.2                 
## [113] spatstat.geom_2.3-0         plotly_4.10.0              
## [115] rmarkdown_2.11              grid_4.1.3                 
## [117] data.table_1.14.2           digest_0.6.29              
## [119] xtable_1.8-4                tidyr_1.1.4                
## [121] httpuv_1.6.3                stats4_4.1.3               
## [123] munsell_0.5.0               viridisLite_0.4.0          
## [125] bslib_0.3.1

---

1. Institute of Psychiatry and Neuroscience of Paris, INSERM U1266, 75014, Paris, France, matthieu.moreau@inserm.fr↩︎